JMLR 2013

116 papers

Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels

Kenji Fukumizu, Le Song, Arthur Gretton

Joint Harmonic Functions and Their Supervised Connections

Mark Vere Culp, Kenneth Joseph Ryan

How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis

Alberto N. Escalante-B., Laurenz Wiskott

Efficient Program Synthesis Using Constraint Satisfaction in Inductive Logic Programming

John Ahlgren, Shiu Yin Yuen

A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion

Tony Cai, Wen-Xin Zhou

Classifier Selection using the Predicate Depth

Ran Gilad-Bachrach, Christopher J.C. Burges

Classifying With Confidence From Incomplete Information

Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, Dun Yu Hsiao

Learning Trees from Strings: A Strong Learning Algorithm for some Context-Free Grammars

Alexander Clark

Lovasz theta function, SVMs and Finding Dense Subgraphs

Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi

Comment on "Robustness and Regularization of Support Vector Machines" by H. Xu et al. (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009)

Yahya Forghani, Hadi Sadoghi

Learning Theory Analysis for Association Rules and Sequential Event Prediction

Cynthia Rudin, Benjamin Letham, David Madigan

Consistent Selection of Tuning Parameters via Variable Selection Stability

Wei Sun, Junhui Wang, Yixin Fang

Sparse Matrix Inversion with Scaled Lasso

Tingni Sun, Cun-hui Zhang

PC Algorithm for Nonparanormal Graphical Models

Naftali Harris, Mathias Drton

Communication-Efficient Algorithms for Statistical Optimization

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

Fast MCMC Sampling for Markov Jump Processes and Extensions

Vinayak Rao, Yee Whye Teh

Multivariate Convex Regression with Adaptive Partitioning

Lauren A. Hannah, David B. Dunson

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering

GURLS: A Least Squares Library for Supervised Learning

Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco

Divvy: Fast and Intuitive Exploratory Data Analysis

Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten

QuantMiner for Mining Quantitative Association Rules

Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod

Large-scale SVD and Manifold Learning

Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry Rowley

Algorithms and Hardness Results for Parallel Large Margin Learning

Philip M. Long, Rocco A. Servedio

Stationary-Sparse Causality Network Learning

Yuejia He, Yiyuan She, Dapeng Wu

Experiment Selection for Causal Discovery

Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer

A Plug-in Approach to Neyman-Pearson Classification

Xin Tong

Multi-Stage Multi-Task Feature Learning

Pinghua Gong, Jieping Ye, Changshui Zhang

Parallel Vector Field Embedding

Binbin Lin, Xiaofei He, Chiyuan Zhang, Ming Ji

A Near-Optimal Algorithm for Differentially-Private Principal Components

Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha

The CAM Software for Nonnegative Blind Source Separation in R-Java

Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

Manfred Opper, Ulrich Paquet, Ole Winther

A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems

Daniil Ryabko, Jeremie Mary

Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees

Nima Noorshams, Martin J. Wainwright

BudgetedSVM: A Toolbox for Scalable SVM Approximations

Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang

Optimally Fuzzy Temporal Memory

Karthik H. Shankar, Marc W. Howard

Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory

Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto

Maximum Volume Clustering: A New Discriminative Clustering Approach

Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama

Keep It Simple And Sparse: Real-Time Action Recognition

Sean Ryan Fanello, Ilaria Gori, Giorgio Metta, Francesca Odone

Efficient Active Learning of Halfspaces: An Aggressive Approach

Alon Gonen, Sivan Sabato, Shai Shalev-shwartz

One-shot Learning Gesture Recognition from RGB-D Data Using Bag of Features

Jun Wan, Qiuqi Ruan, Wei Li, Shuang Deng

Learning Bilinear Model for Matching Queries and Documents

Wei Wu, Zhengdong Lu, Hang Li

Greedy Feature Selection for Subspace Clustering

Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk

Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows

Julien Mairal, Bin Yu

Distance Preserving Embeddings for General n-Dimensional Manifolds

Nakul Verma

On the Mutual Nearest Neighbors Estimate in Regression

Arnaud Guyader, Nick Hengartner

Tapkee: An Efficient Dimension Reduction Library

Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia

Orange: Data Mining Toolbox in Python

Janez Demsar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar

The Rate of Convergence of AdaBoost

Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire

Message-Passing Algorithms for Quadratic Minimization

Nicholas Ruozzi, sekhar tatikonda

Gaussian Kullback-Leibler Approximate Inference

Edward Challis, David Barber

Segregating Event Streams and Noise with a Markov Renewal Process Model

Dan Stowell, Mark D. Plumbley

Language-Motivated Approaches to Action Recognition

Manavender R. Malgireddy, Ifeoma Nwogu, Venu Govindaraju

Convex and Scalable Weakly Labeled SVMs

Yu-feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou

Distribution-Dependent Sample Complexity of Large Margin Learning

Sivan Sabato, Nathan Srebro, Naftali Tishby

Construction of Approximation Spaces for Reinforcement Learning

Wendelin Böhmer, Steffen Grünewälder, Yun Shen, Marek Musial, Klaus Obermayer

Approximating the Permanent with Fractional Belief Propagation

Michael Chertkov, Adam B. Yedidia

Machine Learning with Operational Costs

Theja Tulabandhula, Cynthia Rudin

Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting

Nayyar A. Zaidi, Jesús Cerquides, Mark J. Carman, Geoffrey I. Webb

Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation

Daniel Hernández-lobato, José Miguel Hernández-Lobato, Pierre Dupont

Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty

Wei Pan, Xiaotong Shen, Binghui Liu

Distributions of Angles in Random Packing on Spheres

Tony Cai, Jianqing Fan, Tiefeng Jiang

Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs

Matthew J. Urry, Peter Sollich

Variable Selection in High-Dimension with Random Designs and Orthogonal Matching Pursuit

Antony Joseph

On the Convergence of Maximum Variance Unfolding

Ery Arias-Castro, Bruno Pelletier

Raman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel

Nonparametric Sparsity and Regularization

Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Alessandro Verri

Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos

Anastasios Roussos, Stavros Theodorakis, Vassilis Pitsikalis, Petros Maragos

Dimension Independent Similarity Computation

Reza Bosagh Zadeh, Ashish Goel

Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion

Rami Mahdi, Jason Mezey

Training Energy-Based Models for Time-Series Imputation

Philemon Brakel, Dirk Stroobandt, Benjamin Schrauwen

Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling

Shusen Wang, Zhihua Zhang

A Risk Comparison of Ordinary Least Squares vs Ridge Regression

Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar

Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators

Pierre Neuvial

JKernelMachines: A Simple Framework for Kernel Machines

David Picard, Nicolas Thome, Matthieu Cord

Finding Optimal Bayesian Networks Using Precedence Constraints

Pekka Parviainen, Mikko Koivisto

Multicategory Large-Margin Unified Machines

Chong Zhang, Yufeng Liu

Stochastic Variational Inference

Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley

Regularization-Free Principal Curve Estimation

Samuel Gerber, Ross Whitaker

Random Spanning Trees and the Prediction of Weighted Graphs

Nicolò Cesa-bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella

Manifold Regularization and Semi-supervised Learning: Some Theoretical Analyses

Partha Niyogi

Performance Bounds for λ Policy Iteration and Application to the Game of Tetris

Bruno Scherrer

GPstuff: Bayesian Modeling with Gaussian Processes

Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen

Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing

Lisha Chen, Andreas Buja

Sparse Activity and Sparse Connectivity in Supervised Learning

Markus Thom, Günther Palm

Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications

Ming-Jie Zhao, Narayanan Edakunni, Adam Pocock, Gavin Brown

Bayesian Canonical Correlation Analysis

Arto Klami, Seppo Virtanen, Samuel Kaski

Query Induction with Schema-Guided Pruning Strategies

Joachim Niehren, Jérôme Champavère, Aurélien Lemay, Remi Gilleron

Truncated Power Method for Sparse Eigenvalue Problems

Xiao-Tong Yuan, Tong Zhang

A Widely Applicable Bayesian Information Criterion

Sumio Watanabe

Quasi-Newton Method: A New Direction

Philipp Hennig, Martin Kiefel

Greedy Sparsity-Constrained Optimization

Sohail Bahmani, Bhiksha Raj, Petros T. Boufounos

MLPACK: A Scalable C++ Machine Learning Library

Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram

Semi-Supervised Learning Using Greedy Max-Cut

Jun Wang, Tony Jebara, Shih-fu Chang

Sparsity Regret Bounds for Individual Sequences in Online Linear Regression

Sébastien Gerchinovitz

Differential Privacy for Functions and Functional Data

Rob Hall, Alessandro Rinaldo, Larry Wasserman

Bayesian Nonparametric Hidden Semi-Markov Models

Matthew J. Johnson, Alan S. Willsky

CODA: High Dimensional Copula Discriminant Analysis

Fang Han, Tuo Zhao, Han Liu

A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics

Hervé Frezza-Buet, Matthieu Geist

Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality

Sébastien Bubeck, Damien Ernst, Aurélien Garivier